@inproceedings{a3d72b6d378343c8ae1dfc290f7054ec,
title = "Deep learning based topic identification and categorization: Mining diabetes-related topics on Chinese health websites",
abstract = "As millions of people are diagnosed with diabetes every year, the demand for information about diabetes continues to increase. China is one of the countries with a large population of diabetes patients. Many Chinese health websites provide diabetes related news and articles. However, because most of the online articles are uncategorized or lack a clear topic and theme, users often cannot find their topics of interest effectively and efficiently. The problem of health topic identification and categorization on Chinese websites cannot be easily addressed by applying existing approaches and methods, which have been used for English documents, in a straightforward manner. To address this problem and meet users{\textquoteright} demand for diabetes related information needs, we propose a deep learning based framework to identify and categorize topics related to diabetes in online Chinese articles. Our experiments using datasets with over 19,000 online articles showed that the framework achieved a higher effectiveness and accuracy in categorizing diabetes related topics than most of the state-of-the-art benchmark approaches.",
keywords = "Chinese, Deep learning, Healthcare, Text classification",
author = "Xinhuan Chen and Yong Zhang and Jennifer Xu and Chunxiao Xing and Hsinchun Chen",
note = "Funding Information: According to a research report, vegan diet is more helpful than the diet recommended by the American Diabetes Association for preventing cardiovascular complications in diabetes patients. About two-thirds diabetes patients die of heart diseases or strokes. Thus the prevention of cardiovascular disease is a top priority. The study was funded by the U.S. Physicians Committee for Responsible Medicine, which recommended vegan diet. Funding Information: The research presented in this paper has been funded by grants from the National High-tech R&D Program of China (Grant No. SS2015AA020102), the US NSF grant (IIP-1417181), the 1000-Talent program, and Tsinghua University Initiative Scientific Research Program. Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 21st International Conference on Database Systems for Advanced Applications, DASFAA 2016 ; Conference date: 16-04-2016 Through 19-04-2016",
year = "2016",
doi = "10.1007/978-3-319-32025-0_30",
language = "English (US)",
isbn = "9783319320243",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "481--500",
editor = "Navathe, {Shamkant B.} and Weili Wu and Shashi Shekhar and Xiaoyong Du and Hui Xiong and Wang, {X. Sean}",
booktitle = "Database Systems for Advanced Applications - 21st International Conference, DASFAA 2016, Proceedings",
}